Instructions to use omid-ebi/Bert_Finetuning_Text_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omid-ebi/Bert_Finetuning_Text_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="omid-ebi/Bert_Finetuning_Text_Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("omid-ebi/Bert_Finetuning_Text_Classification") model = AutoModelForSequenceClassification.from_pretrained("omid-ebi/Bert_Finetuning_Text_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from omid-ebi/Bert_Finetuning_Text_Classification: direct link, hf CLI and curl.
- Browser
- Download file 652 MB
-
https://huggingface.co/omid-ebi/Bert_Finetuning_Text_Classification/resolve/main/model.safetensors
- Command line
-
hf download hf://omid-ebi/Bert_Finetuning_Text_Classification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/omid-ebi/Bert_Finetuning_Text_Classification/resolve/main/model.safetensors
652 MB
- Xet hash:
- bd1b319b5c07b381fe872c08923e168db268114528657607cc1b169dcbfa1cfa
- Size of remote file:
- 652 MB
- SHA256:
- 7a10e3809e7817bd33cb42d0067d03845639b45417e9b4dd38242bf67c032cc4
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